A systems biology approach to multi-faceted diseases has provided an opportunity to establish a holistic understanding of the processes at play. Thus, the current study merges transcriptomics and metabonomics data in order to improve diagnostics, biomarker identification and to explore the possibilities of a molecular phenotyping of ulcerative colitis (UC) patients. Biopsies were obtained from the descending colon of 43 UC patients (22 active UC and 21 quiescent UC) and 15 controls. Genome-wide gene expression analyses were performed using Affymetrix GeneChip Human Genome U133 Plus 2.0. Metabolic profiles were generated using (1)H Nuclear magnetic resonance spectroscopy (Bruker 600 MHz, Bruker BioSpin, Rheinstetten, Germany). Data were analyzed with the use of orthogonal-projection to latent structure-discriminant analysis and a multivariate logistic regression model fitted by lasso. Prediction performance was evaluated using nested Monte Carlo cross-validation. The prediction performance of the merged data sets and that of relative small (<20 variables) multivariate biomarker panels suggest that it is possible to discriminate between active UC, quiescent UC, and controls; between patients with or without steroid dependency, as well as between early or late disease onset. Consequently, this study demonstrates that the novel approach of integrating metabonomics and transcriptomics combines the better of the two worlds, and provides us with clinical applicable candidate biomarker panels. These combined panels improve diagnostics and more importantly also the molecular phenotyping in UC and provide insight into the pathophysiological processes at play, making optimized and personalized medication a possibility.